Support Vector Machine (SVM) is a very competitive linear classifier based on convex optimization problem, were support vectors fully describe decision boundary. Hence, SVM is sensitive to data spread and does not take into account the existence of class subclasses, nor minimizes data dispersion for classification performance improvement. Thus, Kernel subclass SVM (KSSVM) was proposed to handle multimodal data and to minimize data dispersion. Nevertheless, KSSVM has difficulties in classifying sequentially obtained data and handling large scale datasets, since it is based on batch learning. For this reason, we propose a novel incremental KSSVM (iKSSVM) which handles dynamic and large data in a proper manner. The iKSSVM is still based on convex optimization problem and minimizes data dispersion within and between data subclasses incrementally, in order to improve discriminative power and classification performance. An extensive comparative evaluation of the iKSSVM to batch KSSVM, as well as, other contemporary incremental classifiers, on real world datasets, has shown clearly its superiority in terms of classification accuracy.

中文翻译：

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增量子类支持向量机

支持向量机（SVM）是一种非常有竞争力的基于凸优化问题的线性分类器，支持向量充分描述了决策边界。因此，SVM 对数据扩散很敏感，没有考虑类子类的存在，也没有最小化数据分散以提高分类性能。因此，提出了内核子类 SVM (KSSVM) 来处理多模态数据并最小化数据分散。然而，KSSVM 在对顺序获得的数据进行分类和处理大规模数据集方面存在困难，因为它是基于批量学习的。出于这个原因，我们提出了一种新颖的增量 KSSVM (iKSSVM)，它以适当的方式处理动态和大数据。iKSSVM 仍然基于凸优化问题，并逐步最小化数据子类内和数据子类之间的数据分散，以提高判别力和分类性能。在现实世界的数据集上，对 iKSSVM 与批处理 KSSVM 以及其他当代增量分类器的广泛比较评估清楚地表明了它在分类准确性方面的优势。